Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source DataDownload PDF

21 May 2021, 20:43 (modified: 22 Dec 2021, 04:54)NeurIPS 2021 PosterReaders: Everyone
Keywords: Unsupervised Domain Adaptation, Model Adaptation, Contrastive Learning, Segmentation, Detection, Classification, Transfer Learning
TL;DR: In this paper, we explore memory mechanism for unsupervised model adaptation (UMA), or called unsupervised domain adaptation without source data, and propose a novel historical contrastive learning for UMA.
Abstract: Unsupervised domain adaptation aims to align a labeled source domain and an unlabeled target domain, but it requires to access the source data which often raises concerns in data privacy, data portability and data transmission efficiency. We study unsupervised model adaptation (UMA), or called Unsupervised Domain Adaptation without Source Data, an alternative setting that aims to adapt source-trained models towards target distributions without accessing source data. To this end, we design an innovative historical contrastive learning (HCL) technique that exploits historical source hypothesis to make up for the absence of source data in UMA. HCL addresses the UMA challenge from two perspectives. First, it introduces historical contrastive instance discrimination (HCID) that learns from target samples by contrasting their embeddings which are generated by the currently adapted model and the historical models. With the historical models, HCID encourages UMA to learn instance-discriminative target representations while preserving the source hypothesis. Second, it introduces historical contrastive category discrimination (HCCD) that pseudo-labels target samples to learn category-discriminative target representations. Specifically, HCCD re-weights pseudo labels according to their prediction consistency across the current and historical models. Extensive experiments show that HCL outperforms and state-of-the-art methods consistently across a variety of visual tasks and setups.
Supplementary Material: pdf
Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.
10 Replies

Loading